ONNX-Go: neural networks made easy
Feel free to reach out on Twitter or Gopher slack: @owulveryck. The main project is hosted here: https://github.com/owulveryck/onnx-go If you want to test the code, here are the links: # Get the model: curl https://www.cntk.ai/OnnxModels/mnist/opset_7/mnist.tar.gz | tar -C /tmp -xzvf - # Get the demo binary from https://github.com/owulveryck/onnx-go/releases/tag/v0.1-mnist-cli # Run it: ./mnist-re
Optimizing Go programs by AVX2 using Auto-Vectorization in LLVM.
I found an interesting article written about optimizations for Arrow Go. It looks that this method can be applied to wide variety of Go projects which needs arithmetic vector operations. The optimization is clearly explained in the above article. Here I give more detailed introduction and how I fixed problems that I bumped into. After reading this article, you will know the followings: What is SIM
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GitHub - antonmedv/watch: watch tool rewritten in go
GitHub - webpod/red: Terminal log analysis tools
GitHub - RRethy/vim-hexokinase: hexokinase.vim - (Neo)Vim plugin for asynchronously displaying the colours in the file (#rrggbb, #rgb, rgb(a)? functions, hsl(a)? functions, web colours, custom patterns)
